1. Field of the Invention
The present invention generally relates to online selling of products or services over a computer network, and more particularly, to a method for purchasing and selling products or services in a networked environment using a request for quotation process and a rule-based system for configuring sell bids having multiple attributes.
2. Background Description
Commerce over networks, particularly electronic commerce (e-commerce) over the Internet, has increased significantly over the past few years. In e-commerce models, buyers and sellers make trades, e.g., buy and sell services or products, over the World Wide Web portion of the Internet. In one example, one or more web pages, typically referred to as an electronic marketplace (e-marketplace), provide one or more different forms of trading mechanisms including auctions, reverse auctions and exchanges. In an auction, one seller receives bids from one or more buyers for one or more products before making a transaction. In contrast, a reverse auction allows one buyer to receive bids from one or more potential sellers. In an exchange, multiple buyers and multiple sellers submit asks and bids, respectively, to a marketplace. The marketplace then makes matches between the asks and bids of the buyers and sellers, either continuously or periodically.
It is known, of course, that these trading models have many different variations. These auction variations may include English (buyers call ascending prices), Dutch (market manager calls descending prices to obtain buy bids), Japanese (market manager calls ascending prices to obtain buy bids) and sealed bid (buyers place sealed bids) auctions. In still other variations of auctions, there is an open Request for Bids and a sealed Request For Bids. In the open Request for Bids, buyers may call ascending prices and a seller manually selects the winning price. In the sealed Request for Bids, buyers submit sealed bids and a seller manually selects the winning bid.
There are also variations on reverse auctions which include reverse English (sellers call descending prices), reverse Dutch (market manager call ascending prices to obtain sell bids), reverse Japanese (market manager calls descending prices to obtain sell bids) and reverse sealed bid (sellers place sealed bids) auctions. Reverse auctions further include open Request For Quotes and sealed Request For Quotes. In the open Request for Quotes, the sellers call descending prices and a buyer manually selects a winning price, and in the sealed Request for Quotes the sellers submit sealed bids and a buyer manually selects the winning quote. Exchanges also include variations. These variations include continuously clearing exchanges and periodically clearing exchanges.
The Request for Quotation (RFQ) is used often in the e-marketplace. In this type of environment, a request is submitted by a buyer to an e-marketplace to invite potential sellers to bid on specific products or services needed by the buyer. The RFQ process is useful in all markets that depend upon attributes other than price such as delivery time, quantity discounts, warranty, seller qualification and other factors. In these RFQ processes, the buyers are permitted to manually select one or more bids from sellers after examining and comparing submitted sell bids. In this manner, the RFQ process allows the sellers to match exactly the buyers' requirements (including the attributes of price, delivery time, warranty, seller qualification and other factors) thus leading to a strong rate of return and high satisfaction ratings.
Depending on the needs of the organization in which a buyer represents, a buyer can submit multiple requests on one or more products or services to one or more electronic marketplaces. Likewise, a seller can find multiple requests on products and services that the seller can fulfill in one or more e-marketplace existing in the Internet. In this situation, a seller needs strategies and tactics for deciding, for example, (i) what requests available in e-marketplaces the seller wants to submit bids to and (ii) what offers the seller wants to make in bids for each of the selected requests. Decisions for these questions need to maximize the revenue and/or profit of the seller and minimize administration cost for RFQ processes, while considering various constraints such as limited inventory, manufacturing capacity, delivery capability and others. The decision-making problem thus becomes one of optimization with objectives, constraints, and a number of unknown variables. Also, it becomes an information search problem.
Rule-based systems, or more generally knowledge-based expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Conventional computer programs perform tasks using conventional decision-making logic containing little knowledge other than the basic algorithm for solving that specific problem and the necessary boundary conditions. This program knowledge is often embedded as part of the programming code, so that as the knowledge changes the program has to be changed and then rebuilt. Knowledge-based systems collect the small fragments of human know-how into a knowledge base which is used to reason through a problem, using the knowledge that is appropriate.
Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, or “rules of thumb,” which specify a set of actions to be performed for a given situation. A rule is composed of an “if ” portion and a “then ” portion. The “if” portion of a rule is a series of patterns which specify the facts (or data) which cause the rule to be applicable. (The process of matching facts to patterns is called pattern matching.) The expert system tool provides a mechanism, called the inference engine or the shell, which automatically matches facts against patterns and determines which rules are applicable. The “if” portion of a rule can actually be thought of as the “whenever” portion of a rule since pattern matching always occurs whenever changes are made to facts. The “then” portion of a rule is the set of actions to be executed when the rule is applicable. The actions of applicable rules are executed when the inference engine is instructed to begin execution. The inference engine selects a rule and then the actions of the selected rule are executed (which may affect the list of applicable rules by adding or removing facts). The inference engine then selects another rule and executes its actions. This process continues until no applicable rules remain.
Problems with the Prior Art
One problem with the prior art for deciding what offers to make in bids for selected requests for quotation from buyers is that it requires a seller to manually decide and specify the details of offers the seller makes for individual requests. This approach requires the seller to understand and remember a large amount of detailed information such as inventory data, fulfillment system data, buyer data, historical data about RFQ processes, and the current and historical market data that involves in the realization of the seller's objectives such as maximizing revenue/profit while minimizing administration cost, and to apply the information to configuring individual sell bids. This approach is not effective nor realistic due to the amount of involved information and the complexity of the optimization for achieving the objectives. Although it is possible to achieve relatively effective results with this manual ad hoc approach, it does not explain different results that would be caused by taking alternative options.